Prediction of Facebook Post Metrics using Machine Learning

TL;DR

This paper compares three machine learning methods—SVR, ESN, and ANFIS—for predicting Facebook post impact, aiming to develop models that can forecast social media influence effectively.

Contribution

It evaluates and compares the effectiveness of three different machine learning techniques for social media impact prediction using a benchmark dataset.

Findings

SVR outperforms other methods in accuracy

ESN shows faster training times

ANFIS provides interpretable models

Abstract

In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook. Social medias have a huge influence in the social behaviour. Therefore to develop an automatic model for predicting the impact of posts in social medias can be useful to the society. In this article, we analyze the efficiency for predicting the post impact of three popular techniques: Support Vector Regression (SVR), Echo State Network (ESN) and Adaptive Network Fuzzy Inject System (ANFIS). The evaluation was done over a public and well-known benchmark dataset.